Patient treatment recommendations

Abstract

Data associated with a patient undergoing medical treatment can be received. The data can pertain to a use of the medical treatment and a state of the patient. A subset of the data pertaining to the state of the patient can be identified as context. A function can be learned that relates the context to a reward derived from the medical treatment. The function can be used, based on a current state of the patient, to identify a type of treatment to deliver to the patient.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 receiving data associated with a patient undergoing a medical treatment, the data pertaining to use of the medical treatment and a state of the patient;   identifying a subset of the data pertaining to the state of the patient as context;   learning a function that relates the context to a reward derived from the medical treatment; and   using the function based on a current state of the patient to identify a type of treatment to deliver to the patient.   
     
     
         2 . The method of  claim 1 , wherein the medical treatment is a self-administered medical treatment where the medical treatment is initiated in clinic and subsequently self-applied by the patient. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the medical treatment is performed using a wearable medical device that the patient is wearing. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein the reward is related to a metric structured as a multi-dimensional vector that describes the state of the patient related to the medical treatment the patient is undergoing. 
     
     
         5 . The computer-implemented method of  claim 4 , wherein the reward is a scalar function of the context. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the subset of data includes a set of features from the received data. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the function is learned using a contextual multi-armed bandit robust with respect to super-gaussian noise. 
     
     
         8 . A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to cause the device to:
 receive data associated with a patient undergoing a medical treatment, the data pertaining to use of the medical treatment and a state of the patient;   identify a subset of the data pertaining to the state of the patient as context;   learn a function that relates the context to a reward derived from the medical treatment; and   use the function based on a current state of the patient to identify a type of treatment to deliver to the patient.   
     
     
         10 . The computer program product of  claim 8 , wherein the medical treatment is a self-administered medical treatment where the medical treatment is initiated in clinic and subsequently self-applied by the patient. 
     
     
         11 . The computer program product of  claim 8 , wherein the medical treatment is performed using a wearable medical device that the patient is wearing. 
     
     
         12 . The computer program product of  claim 8 , wherein the reward is related to a metric structured as a multi-dimensional vector that describes the state of the patient related to the medical treatment the patient is undergoing. 
     
     
         13 . The computer program product of  claim 12 , wherein the reward is a scalar function of the context. 
     
     
         14 . The computer program product of  claim 8 , wherein the subset of data includes a set of features from the received data. 
     
     
         15 . The computer program product of  claim 8 , wherein the function is learned using a contextual multi-armed bandit robust with respect to super-gaussian noise. 
     
     
         16 . A system comprising:
 at least one processor; and   at least one memory device coupled with the at least one processor;   the at least one processor configured to at least:
 receive data associated with a patient undergoing a medical treatment, the data pertaining to use of the medical treatment and a state of the patient; 
 identify a subset of the data pertaining to the state of the patient as context; 
 learn a function that relates the context to a reward derived from the medical treatment; and 
 use the function based on a current state of the patient to identify a type of treatment to deliver to the patient. 
   
     
     
         17 . The system of  claim 16 , wherein the medical treatment is a self-administered medical treatment where the medical treatment is initiated in clinic and subsequently self-applied by the patient. 
     
     
         18 . The system of  claim 16 , wherein the medical treatment is performed using a wearable medical device that the patient is wearing. 
     
     
         19 . The system of  claim 16 , wherein the reward is related to a metric structured as a multi-dimensional vector that describes the state of the patient related to the medical treatment the patient is undergoing. 
     
     
         20 . The system of  claim 16 , wherein the function is learned using a contextual multi-armed bandit robust with respect to super-gaussian noise.